Fleet Operations Associate Manager Jobs

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We’re looking for a Rack Power Engineer with deep expertise in high-power conversion and distribution to design, qualify, and support power systems for AI supercomputers. You will own rack power solutions—including power shelves, AC/DC rectifiers, power supply units (PSUs), power management controllers (PMCs), and high-current distribution—from requirements and supplier development through deployment. You will also monitor fleet rack power health, lead debugging and root-cause investigations, and drive improvements into hardware, firmware, and qualification coverage. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own rack power architecture and requirements for high-power AI supercomputing systems, including power budgets, AC input interfaces, DC distribution, redundancy, efficiency, serviceability, and integration with data center infrastructure. Drive the design and supplier development of power shelves, rectifiers, PSUs, PMCs, busbars, connectors, and protection circuits. Review electrical designs and control behavior, and evaluate performance, cost, reliability, and availability trade-offs. Define and execute component, shelf, and rack qualification plans covering load transients, current sharing, hot-swap, startup and shutdown, redundancy failover, fault protection and recovery, thermal limits, and AC disturbances and ride-through

About the Team The Fleet team builds core components to enable productive research from small to state of the art scale across OpenAI, with the goal of accelerating progress towards AGI. We frequently collaborate with other teams to speed up the development of new state-of-the-art capabilities. About the Role As we scale up with more researchers and engineers joining OpenAI, we seek a pragmatic and passionate engineer with a strong focus on the development experience for both engineers and scientists. In this role, you will be responsible for building and maintaining systems that allow our research + engineering organization to iteratively develop, test, and deploy new features reliably, with high velocity, and with a frictionless and fast development cycle. You will help oversee and drive to the vision of how we should build, test and deploy software. You will drive the design of our continuous integration pipelines, testing infrastructure, training and support around our build system. Our current environment relies heavily on Python, Rust, and C++, which you will take ownership of and strive to transform into a state of the art development experience for research. Ultimately, your role will be to provide the necessary tools and metrics to support our fast-paced culture and ensure a stable, scalable platform for growth, while also fostering a seamless and low friction experience for OpenAI’s research. This role is based in San Francisco, CA. For a San Francisco role, we use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. You might thrive in this role if you: Have supported large monorepo development and deployment before Are a proficient Python programmer working in large monorepos Are proficient with Docker and Kubernetes Experienced in CI/CD About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boun

About the Team The compute infrastructure team runs the GPU fleet and large-scale compute clusters that serve the models backing ChatGPT and the API, while also supporting training workloads for our next generation models. We operate a large, modern GPU fleet and provide a unified platform for other OpenAI teams to seamlessly run production Applied AI and Research training workloads. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth. About the Role You will be part of an engineer-first TPM team as a Technical Program Manager for Compute Infrastructure who owns the end-to-end delivery of large-scale GPU clusters, partnering with engineers to bring clusters online across external providers and partners. You’ll run a broad, parallel portfolio spanning hardware, networking, power, and cooling—driving execution, risk management, and crisp alignment from working teams through leadership to deliver production-ready capacity at scale. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Lead end-to-end delivery of both New Compute SKUs and large-scale GPU clusters across an external partner ecosystem while supporting capacity planning for training and inference. Ability to contextually drive multi-threaded bring-up programs spanning hardware, networking, power, and cooling—owning plans, dependencies, and critical paths. Interface with chip providers to derisk long-term onboarding to new hardware platforms by working across kernels, comms, hardware, and scheduling engineering teams. Build and operationalize program mechanisms (roadmaps, milestones, risk registers, runbooks) that make delivery predictable at massive scale. Partner with engineering to improve cluster turn-up reliability, repeatability, and automation

About the Role We are seeking a Strategic Finance Lead for our GPU compute fleet. In this role, you will be a key partner to our inference and infrastructure teams, providing financial expertise to optimize our compute investments and drive capacity decisions. In this role, you'll develop deep expertise in the economics of AI compute, from token-level serving cost to the long-range financial planning of our GPU fleet. You'll build the models that inform Perplexity's capacity and make-versus-buy decisions, own the internal-cost analysis that underpins internal and external token pricing, and translate complex infrastructure dynamics into clear financial narratives for leadership. This is a high-impact role for someone who thrives at the intersection of finance and infrastructure, and who is energized by building frameworks from scratch in a fast-moving environment. This position is based in San Francisco and requires in-person attendance 2-3 days per week. Key Responsibilities Finance lead for GPU compute spend, including budgeting, monthly forecasting, variance analysis, and financial plan maintenance Build and maintain detailed bottoms-up financial models for the GPU fleet, including capacity forecasts, cost driver analyses, and investment scenario modeling Develop deep expertise in GPU vendor contracts, pricing structures, and cost drivers, and surface optimization opportunities across the fleet Serve as subject matter expert for Perplexity's compute capacity plan, owning source of truth on utilization, committed-versus-consumed spend, and capacity by vendor and cluster Build internal token-cost curves distinguishing marginal from fully loaded serving cost, and translate them into internal and external token pricing Analyze the ROI of in-house inference and training, including opportunity cost across chip types, cluster configurations, and workloads, distilled into a framework for capacity deployment Partner closely with inference and infrastructure engineering to

About Us At BGBx , we're driven by a simple idea: breakthrough thinking creates breakthrough impact. As an independent commercial solutions partner to pharmaceutical and life science companies, we bring together strategists, scientists, communicators, creatives, technologists, and data experts to tackle some of healthcare's most important challenges. Our teams work across consulting and communications to help clients shape strategy, launch innovations, engage stakeholders, and drive meaningful results throughout the product lifecycle. The work is complex, fast-moving, and deeply connected to improving lives, creating opportunities for curious minds to make a real difference every day. If you're energized by collaboration, inspired by innovation, and motivated by work that matters, you'll find a place to grow, contribute, and make a meaningful impact at BGBx. Position Overview We are looking for an AI Engineer with 3-5 years of experience to help maintain and evolve our fleet of AI agents, LLM-as-a-judge systems, and other AI-powered tools used by healthcare, life sciences, and marketing teams. This role will work closely with senior engineers on graph design and with product and research teams to define what “a good answer” means, including grounding, coverage, and citation honesty. The AI Engineer will contribute to the continued development of these systems by improving tools, retrieval and citations, agent architecture, and overall system speed, cost, and accuracy. They will also support evaluations and experiments that help us understand and improve system performance. We are looking for someone with strong Python skills and a solid understanding of LLM systems who is excited to continue growing their technical depth. At this level, the engineer should be comfortable owning a component of a production service from design through testing and review, while partnering with more senior engineers on b

Zoox’s Advanced Hardware Engineering team is in charge of delivering all the autonomous hardware, infotainment electronic control units and electrical components that go into Zoox’s future fleet of self-driving vehicles. At the heart of our hardware architecture is the Primary Compute Unit (PCU) - the compute system that runs the AI stack that enables the vehicle to drive autonomously. We are looking for a seasoned technical program manager own the PCU program end-to-end, including the integrated program schedule. In this role, you will work cross-functionally with our vehicle engineering, systems engineering, software, safety, supply chain, quality, manufacturing and integration teams to achieve our strategic objectives and tactical milestones. You will develop project schedules with numerous dependencies, map program milestones to company milestones, flag and mitigate risks, estimate and maintain budgets, identify solutions to complex problems, navigate ambiguity, all the while holding teams accountable and clearly communicating expectations and ongoing progress. As with any startup, we move fast here at Zoox, and many candidates will be asked from time to time to wear many hats. We would love to hear from you if you are up for solving very challenging problems across numerous cross-functional teams, delivering milestones with numerous dependencies, and scaling your impact by developing and adopting structured ways of working. Additionally, if you are an expert at herding cats and influencing without formal authority, then this role was made for you! In this role, you will: Drive the PCU program forward with thought and energetic leadership, including supporting architecture alignment, hardware development, validation, integration, cross-functional alignment and company-wide communication. This will be done in close partnership with the technical lead and other core team members across multiple departments. Work with department le

Traditional engineering roles hand you a task and ask you to implement it. This one hands you a problem and a fleet of AI agents and asks you to ship the solution. The Product Engineer / Builder is a Staff/Principal level individual contributor role , for someone who takes a defined problem and owns the solution end to end. You understand the customer and the problem, then design, build, and ship working software, using AI agents as your primary means of turning direction into product. You combine deep engineering craft with genuine product instinct, and act as a force multiplier rather than a single pair of hands. How You’ll Work Given a problem to solve, you shape the approach and use AI to bring it to life by directing several AI agents in parallel and rigorously reviewing what they produce. An idea sketched in the morning can be a functional prototype by lunch and a working product by the end of the week . You think like a product manager, care like a designer, and build like an engineer. This is not a role for someone who waits to be told exactly what to type. You bring taste and judgment to every decision. It’s your responsibility to know what to build, what not to build, and to prove — through evals, testing, and review — that AI-generated work is reliable enough to ship. You measure success in customer and business outcomes, not tasks completed. Build on What Works, Invent Where It Counts Build rapidly by composing solutions from the best available tools, whether internal or external, and apply your own innovation where it matters most. That means drawing on proven platforms, components, services, and models instead of building everything from scratch, then adding original thinking where there are gaps to close, experiences to elevate, or clear opportunities to differentiate. You know when to leverage what already works, when to extend it, and wh

About the Team Our team analyzes inference stack performance across the application, model, and fleet layers to identify bottlenecks and drive faster, cheaper inference. We combine systems profiling, benchmarking, and analysis to understand where time and cost are spent, then turn that understanding into performance optimizations and models that project performance and capacity needs for future launches. About the Role In this role, you will model inference performance across application, model, and fleet layers with higher fidelity. You will build cost-to-serve estimates from microbenchmarks and create tools that help cross-functional teams reason about latency, capacity, utilization, and cost tradeoffs. In this role, you will Build and refine performance models that translate microbenchmark results into cost-to-serve estimates. Analyze inference workloads end to end across applications, models, and fleet infrastructure. Enhance tooling to identify bottlenecks across layers for latency and throughput. Partner with other teams to turn performance insights into concrete improvements and project how future changes affect inference. You might thrive in this role if you: Enjoy reasoning from first principles about distributed systems, model inference, and hardware efficiency. Are comfortable working across abstraction layers, from application behavior to kernels, accelerators, networking, and fleet scheduling. Have deep expertise with performance profiling, benchmarking, analysis, and optimization. Enjoy collaborating with engineering and research teams to improve real production systems. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve o

NVIDIA DGX Cloud is an AI Factory designed to power the next generation of AI and industrial-scale breakthroughs. As the Distinguished Engineer for Security Architecture, within our Security Engineering organization, you will set the security design bar for an AI factory of hundreds of thousands of GPUs, and then build against it alongside the teams. This is the founding architecture seat in a new organization. Security Engineering is a new organization at DGX Cloud, accountable for the security outcome of the platform, and this is the architecture function inside it. You will define the security design standard for DGX Cloud, a bar that sits above the company floor, and hold it from inside the teams doing the building. Security here is fleet horizontal and stack vertical, so your scope runs from the hardware root of trust and the hardened baseline, through tenancy and GPU workload isolation, to the services and APIs built on top, across every DGX Cloud engineering organization. A small team of Principal Engineers will report to you and hold the bar at domain depth. This is still a hands-on seat, and you stay in the design with them. You will also serve as DGX Cloud's technical interface into NVIDIA's central security organization. There is no architecture review board here and no approval queue; the bar holds because the strongest security engineers in the room helped set it and helped ship it. What You Will Be Doing: Set the DGX Cloud Security Bar: Own the security design standard across DGX Cloud (tenancy, GPU workloads, identity, supply chain, and isolation) and make it concrete. Reference architectures, golden paths, and requirements engineers can actually build against, not a policy library. Hold the Bar by Building: Embed with engineering teams on real work: join the design, learn the code, help ship the thing rather than grade it afterward.

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. ABOUT THE ROLE: As a Site Reliability Engineer focused on campus reliability, you will design what the campus watches and trusts, technically command cross-discipline SEVs, and build the guardrails that make the next incident smaller. You are the connective tissue across compute, network, storage, power, and cooling on one of the largest AI training campuses in the world. This role demands calm incident leadership, fleet-scale observability judgment, and the ability to drive reliability work across software and facility boundaries. We are looking for candidates from power plants, nuclear power plants, data centers, or people who currently work or have worked in facilities like SpaceXSI with power, compute, cooling, and related plant systems. RESPONSIBILITIES: Own monitoring architecture and signal quality across GPU and host telemetry (DCGM, Prometheus/Grafana) and facility telemetry (BMS, EPMS, DCIM, SCADA, CDU / direct liquid cooling): what we alert on, suppress, and trust. Consume NOC noise-disposition feedback to drive suppression and redesign. Treat alert noise as a design failure, not an operator failure. Provide SEV command support: technical incident leadership, bridge c

NVIDIA DGX Cloud is an AI Factory designed to power the next generation of AI and industrial-scale breakthroughs. As a Principal Engineer for Security Architecture, within our Security Engineering organization, you will own a core security domain of the AI factory: the architecture, the paved road that delivers it, and much of the code underneath. You will hold the security design bar across DGX Cloud from inside the teams doing the building, and this is a founding seat on a new team. Security Engineering is a new organization at DGX Cloud, accountable for the security outcome of the platform, and Security Architecture is the function inside it that holds the design bar. Security here is fleet horizontal and stack vertical, so your work will cross every DGX Cloud engineering organization: you will embed with the teams building GPU clusters, control planes, and services, join their designs as a participant rather than an approver, and leave behind systems in which an entire class of risk is no longer possible. There is no architecture review board here and no approval queue. You are a senior IC with deep security domain knowledge, and the security bar holds because you helped set it and then helped ship it. What You Will Be Doing: Own a Security Domain End to End: Take architectural ownership of a core domain of DGX Cloud security, from the design through the system running in production. That could be tenant and GPU workload isolation, workload identity, infrastructure and network, supply-chain provenance, hardened baselines and patching, or deploy-time policy and admission control. Embed with the Teams Building It: Join the design early, write the code, and help land it. The posture is not "you did this wrong." It is "here are the considerations we need to meet, I will help, let's go to work." Build Paved Roads, Not

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Responsibilities Strategize and lead productivity, yield, quality, and cost improvement engagements with global sites and the network team via plasma process optimization, new/FOAK chemistry & hardware qualification , and CIP development collaboration with dry etch suppliers. Collaborate with cross-functional teams to drive breakthroughs in defect reduction, particle/residue control, CD uniformity, profile control, and chamber-matching across the global fleet. Develop the process and hardware CIP roadmap for Dry Etch — covering chamber parts, RF/match networks, gas delivery, ESC, endpoint, and plasma source upgrades. Drive process & hardware improvement with emphasis on safety, defect reduction, cost efficiency, throughput, quality, and sustainability across Dry Etch modules ( Conductor Etch, Dielectric Etch, Poly/Gate Etch, Metal Etch, High-Aspect-Ratio (HARC) Etch, Spacer/ALE, etc.). Partner with suppliers to establish equipment start-up acceptance criteria, chamber qualification flows, and tool service requirements ; work with global facilities to address new tool installation requirements (abatement, exhaust, RF compliance, gas/chemical infrastructure). Improve tool efficiency, WPD, and utilization across all Micron Dry Etch fleets through benchmarking, MAXOUT-style productivity programs, and pathfinding solutions . Set up IoT/FDC sensors and advanced chamber telemetry for defen

At Zoox, we are developing a purpose built autonomous robotaxi, and the Sustaining Engineering team plays a critical role in ensuring the long term reliability, safety, serviceability, and availability of our fleet as it operates at scale. As a Sustaining Engineer supporting Body and Closure Systems, you will independently own sustaining engineering for key subsystems supporting both the in-service fleet and vehicles in current production. You will drive root cause investigations and corrective actions, making subsystem-level technical decisions and leading changes through design, validation, release, and fleet implementation. This role requires close collaboration with Vehicle Development, Manufacturing, Service, Quality, Reliability, and Suppliers in a fast paced and often ambiguous environment. You will prioritize work based on fleet and production impact, delivering solutions that improve safety, durability, fleet uptime, and manufacturing performance. In this role, you will: Own sustaining engineering and corrective action development for body and closure systems across the in service fleet and vehicles in current production, from root cause confirmation through fleet-wide implementation Drive end-to-end resolution of field and manufacturing issues across door systems, latches, actuators, sealing, access panels, glazing, and body structures Apply fleet and production data, including MTBF, repeat failure rates, and downtime drivers, to prioritize work, validate solutions, and drive continuous improvement Lead design updates and system improvements, ensuring robust performance, durability, efficiency, and serviceability in both fleet and production environments Author and manage Engineering Change Requests and Change Actions, and develop and execute validation plans at component, system, and vehicle levels Support and lead retrofit and rework campaigns, and partner with suppliers to resolve quality issues and im

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is an unusually demanding distributed system. Training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. How well that system holds together determines how much of our compute turns into learning, and how quickly the team can try the next idea. As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking, fault tolerance, autoscaling, or the observability that tells us what a run is actually doing. We're looking for generalists: engineers who can move between these layers, reason from first principles about a system they haven't seen before, and pick the problem that matters most rather than the one closest to their prior experience. Our system changes as fast as the research does, correctness under failure matters as much as throughput, and the best solutions often come from understanding the ML workload well enough to know which guarantees it actually needs. Strong candidates have built and run large distributed systems, care about getting the details right, and want to apply that experience to a workload that is very large, very heterogeneous, and changing

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Our Inference team is responsible for building and scaling the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry’s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators. The team has a dual mandate: maximizing compute efficiency to reliably serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms. Inference systems are highly performance sensitive distributed systems. Inference serves hundreds of thousands of customers every day, and the size & span of the inference fleet requires sophisticated routing, scaling, and networking systems. Key responsibilities Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide Develop resilient, flexible systems that adapt in real time to real world events Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators and multiple cloud providers Maximize compute efficiency and optimize cost across the fleet by autoscaling and orchestrating production, research, and experimental workloads across multiple cloud providers Build and operate production-grade deployment pipelines for releasing new models to user